Alzheimer’s disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer’s disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer’s disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer’s disease and cognitively normal subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer’s Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer’s disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

OBJECTIVE:To elucidate the underlying mechanisms between C-reactive protein (CRP) and cardiovascular disease, we examined the association of circulating CRP in healthy reference range (r1.0 mg/dl) measured by high-sensitive CRP assay with the metabolic syndrome (MS). DESIGN: Cross-sectional study of circulating CRP in adult men. SUBJECTS: A total of 3692 Japanese men aged 34-69 y. MEASUREMENTS: Serum CRP, total cholesterol, triglycerides, LDL-cholesterol, fasting glucose, fasting insulin, uric acid, systolic blood pressure, diastolic blood pressure, and body mass index (BMI). RESULTS: There was a statistically significant positive correlation between CRP and BMI (r ¼ 0.25), total cholesterol (r ¼ 0.096), triglycerides (r ¼ 0.22), LDL-cholesterol (r ¼ 0.12), fasting glucose (r ¼ 0.088), fasting insulin (r ¼ 0.17), uric acid (r ¼ 0.13), systolic blood pressure (r ¼ 0.12), and diastolic blood pressure (r ¼ 0.11), and a significant negative correlation of CRP with HDLcholesterol (r ¼ 0.24). After adjusting for age, smoking, and all other components of MS, obesity, hypertriglyceridemia, hyper-LDL-cholesterolemia, diabetes, hyperinsulinemia, and hyperuricemia were significantly associated with both mildly (Z0.06 mg/ dl) and moderately (Z0.11 mg/dl) elevated CRP. Compared with men who had no such components of the MS, those who had one, two, three, four, and five or more components were, respectively, 1.48, 1.84, 1.92, 3.42, and 4.17 times more likely to have mildly elevated CRP levels (trend Po0.001). As for moderately elevated CRP, the same association was observed. CONCLUSIONS: These results indicate that a variety of components of the MS are associated with elevated CRP levels in a systemic low-grade inflammatory state.

Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer’s disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.

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Ag-doped δ-MnO2 catalysts were synthesized using an alcohol-initiated redox precipitation method at room temperature; toluene was used as a probe molecule of volatile organic compounds (VOCs) to evaluate the catalytic activity of the as-prepared catalyst. The catalytic activity evaluation revealed that the activity for catalytic combustion of toluene was much enhanced by Ag doping. The optimized catalyst (1Ag-MnO2) presented the best catalytic activity for toluene combustion, with the conversion of toluene corresponding to 50% (T 50) and 90% (T 90) at just 182 and 190 °C under testing conditions, respectively. In addition, 1Ag-MnO2 exhibited excellent long-term stability and water resistance. A series of techniques were used to characterize the as-prepared catalysts, and the characterizations demonstrated that the enhanced catalytic performance of Ag-MnO2 catalysts was closely associated with the much-increased active oxygen species content generated by Ag doping. Therefore, the alcohol-initiated redox precipitation method is a versatile process to prepare Ag-doped MnO2 catalysts, and the as-prepared Ag-doped MnO2 catalyst is a robust material for the abatement of toluene.

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METS and DM were significant comorbid conditions in lacunar stroke patients and they were associated with stroke recurrence. In patients with lacunar infarcts, a vigilant approach to prevent development of DM in those with METS may be a potential strategy to reduce recurrent strokes.

In this second of two companion articles, we compare the mass isotopomer distribution of metabolites of liver gluconeogenesis and citric acid cycle labeled from NaH 13 CO 3 or dimethyl [1,4-13 C 2 ]succinate. The mass isotopomer distribution of intermediates reveals the reversibility of the isocitrate dehydrogenase ؉ aconitase reactions, even in the absence of a source of ␣-ketoglutarate. In addition, in many cases, a number of labeling incompatibilities were found as follows: (i) glucose versus triose phosphates and phosphoenolpyruvate; (ii) differences in the labeling ratios C-4/C-3 of glucose versus (glyceraldehyde 3-phosphate)/(dihydroxyacetone phosphate); and (iii) labeling of citric acid cycle intermediates in tissue versus effluent perfusate. Overall, our data show that gluconeogenic and citric acid cycle intermediates cannot be considered as sets of homogeneously labeled pools. This probably results from the zonation of hepatic metabolism and, in some cases, from differences in the labeling pattern of mitochondrial versus extramitochondrial metabolites. Our data have implications for the use of labeling patterns for the calculation of metabolic rates or fractional syntheses in liver, as well as for modeling liver intermediary metabolism.This second of two companion articles concentrates on a comparison of the mass isotopomer distributions of metabolites of gluconeogenesis and the citric acid cycle in livers perfused with precursors of [1-13 C]PEP. 2 One substrate was NaH 13 CO 3 that labels liver GNG from lactate or pyruvate via carboxylation and isotopic exchange reactions (1). The second substrate was dimethyl [1,4-13 C 2 ]succinate that labels PEP via reactions of the citric acid cycle and PEPCK. We modulated the rates of GNG from lactate, pyruvate, or [1,[4][5][6][7][8][9][10][11][12][13] C 2 ]succinate using mercaptopicolinate (MPA), an inhibitor of PEPCK (2, 3), or aminooxyacetate (AOA), an inhibitor of the glutamate-aspartate shuttle (4 -6). Our data reveal major incompatibilities in the labeling of gluconeogenic intermediates extracted from the whole rat liver. EXPERIMENTAL PROCEDURESMaterials-The materials and rat liver perfusion experiments are described in detail in the accompanying article (28). Briefly, livers from 18-h fasted rats (180 -220 g) were perfused (7) with nonrecirculating bicarbonate buffer (40 ml/min) containing the following: (i) 40% enriched NaH 13 CO 3 and 5 mM lactate, or 2 mM pyruvate Ϯ 0.3 mM MPA, or 0.5 mM AOA (protocol I), or (ii) dimethyl [1,4-13 C 2 ]succinate Ϯ 0.3 mM MPA (protocol II). In orientation experiments, we found that the labeling of gluconeogenic and CAC intermediates as well as glucose production were two to four times greater with dimethyl [1,4-13 C 2 ]succinate than with [1,4-13 C 2 ]succinate (not shown). Similar ratios in glucose production from dimethyl succinate and succinate were reported by Rognstad (8). Therefore, we conducted all the experiments of this group with 0.5 mM dimethyl [1,4-13 C 2 ]succinate Ϯ 0.3 mM MPA. Sample Preparation-Powdered fr...